Denoised Monte Carlo for option pricing and Greeks estimation

Andrzej Daniluk, Evgeny Lakshtanov, Rafal Muchorski
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Abstract

We present a novel technique of Monte Carlo error reduction that finds direct application in option pricing and Greeks estimation. The method is applicable to any LSV modelling framework and concerns a broad class of payoffs, including path-dependent and multi-asset cases. Most importantly, it allows to reduce the Monte Carlo error even by an order of magnitude, which is shown in several numerical examples.
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用于期权定价和希腊字母估算的去噪蒙特卡洛算法
我们提出了一种新颖的蒙特卡罗误差减小技术,可直接应用于期权定价和希腊估计。该方法适用于任何 LSV 建模框架,涉及广泛的报酬类别,包括路径依赖和多资产情况。最重要的是,该方法可以将蒙特卡洛误差减少一个数量级,这一点已在几个数字实例中得到证明。
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